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https://delta.ncsa.illinois.edu/wp-content/plugins/zotpress/
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1.
Wei, X. et al. Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws. Preprint at https://doi.org/10.48550/ARXIV.2505.06699 (2025).
1.
Fukami, K. & Taira, K. Observable-augmented manifold learning for multi-source turbulent flow data. J. Fluid Mech. 1010, R4 (2025).
1.
Zhang, Y. et al. Atom-by-atom Imaging of Moiré Phasons using Electron Ptychography. Preprint at https://doi.org/10.48550/ARXIV.2505.03060 (2025).
1.
Wan, T., Luo, C., Zhang, Z., Sun, Y. & Wentzcovitch, R. M. Ferroelasticity, shear modulus softening, and the tetragonal-cubic transition in davemaoite. Preprint at https://doi.org/10.48550/ARXIV.2505.01529 (2025).
1.
Yan, J. & Snir, M. LCI: a Lightweight Communication Interface for Efficient Asynchronous Multithreaded Communication. Preprint at https://doi.org/10.48550/ARXIV.2505.01864 (2025).
1.
Underwood, T., Nelson, L. K. & Wilkens, M. Can Language Models Represent the Past without Anachronism? Preprint at https://doi.org/10.48550/ARXIV.2505.00030 (2025).
1.
Nikitin, F., Dunn, I., Koes, D. R. & Isayev, O. GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation. Preprint at https://doi.org/10.48550/ARXIV.2505.00169 (2025).
1.
Bhat, H. S. Second-Order Adjoint Method for Quantum Optimal Control. Preprint at https://doi.org/10.48550/ARXIV.2505.00529 (2025).
1.
Zhang, Y., Vlachos, D. G., Liu, D. & Fang, H. Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation. J. Chem. Inf. Model. 65, 4334–4345 (2025).
1.
Xu, Z., Yan, J., Gupta, A. & Srikumar, V. State Space Models are Strong Text Rerankers. Preprint at https://doi.org/10.48550/ARXIV.2412.14354 (2024).
1.
Pulavarthi, V., Nandal, D., Dan, S. & Pal, D. AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation. Preprint at https://doi.org/10.48550/ARXIV.2406.18627 (2024).
1.
Boyeneni, S., Wu, J. & Most, E. R. Unveiling the electrodynamic nature of spacetime collisions. Preprint at https://doi.org/10.48550/ARXIV.2504.15978 (2025).
1.
Chang, R. et al. Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers. in (2025).
1.
Shankar, S., Mösta, P., Haas, R. & Schnetter, E. 3D full-GR simulations of magnetorotational core-collapse supernovae on GPUs: A systematic study of rotation rates and magnetic fields. Preprint at https://doi.org/10.48550/ARXIV.2504.11537 (2025).
1.
Chan, A. & Tajkhorshid, E. Restricted Surface Diffusion of Cytochromes on Bioenergetic Membranes with Anionic Lipids. Membranes 15, 124 (2025).
1.
Kang, S., Giraldo, F. X. & Camp, S. A GPU-accelerated simulation of rapid intensification of a tropical cyclone with observed heating. Preprint at https://doi.org/10.48550/ARXIV.2504.08157 (2025).
1.
Li, D. & Minkara, M. S. Decoding SP-D and glycan binding mechanisms using a novel computational workflow. Biophysical Journal S000634952500219X (2025) http://doi.org/10.1016/j.bpj.2025.04.007.
1.
Rawlekar, S., Bhatnagar, S. & Ahuja, N. Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations. Preprint at https://doi.org/10.48550/ARXIV.2409.08381 (2024).
1.
Yang, Z. & Shen, L. TempA-VLP: Temporal-Aware Vision-Language Pretraining for Longitudinal Exploration in Chest X-Ray Image. in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 4625–4634 (IEEE, Tucson, AZ, USA, 2025). http://doi.org/10.1109/WACV61041.2025.00454.
1.
Elahi, A., Yan, X. & Chaudhuri, S. Enhancing the OPLS-AA force field for cellulose Iβ: structural stability and surface functionalization capability with the CM5 charge model. Carbohydrate Polymers 360, 123572 (2025).
1.
Chen, H., Zhu, C., Li, Y. & Driggs-Campbell, K. Tool-as-Interface: Learning Robot Policies from Human Tool Usage through Imitation Learning. Preprint at https://doi.org/10.48550/ARXIV.2504.04612 (2025).
1.
Shashidhar, S. et al. YourBench: Easy Custom Evaluation Sets for Everyone. Preprint at https://doi.org/10.48550/ARXIV.2504.01833 (2025).
1.
Liu, Q. et al. Geometry-Informed Neural Operator Transformer. Preprint at https://doi.org/10.48550/ARXIV.2504.19452 (2025).
1.
Gauba, A. et al. AgMMU: A Comprehensive Agricultural Multimodal Understanding and Reasoning Benchmark. Preprint at https://doi.org/10.48550/ARXIV.2504.10568 (2025).
1.
Lin, L., Yu, X., Pang, Z. & Wang, Y.-X. GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation. Preprint at https://doi.org/10.48550/ARXIV.2504.07962 (2025).
1.
Liu, Q., Koric, S., Abueidda, D., Meidani, H. & Geubelle, P. Towards Signed Distance Function based Metamaterial Design: Neural Operator Transformer for Forward Prediction and Diffusion Model for Inverse Design. Preprint at https://doi.org/10.48550/ARXIV.2504.01195 (2025).
1.
Rapp, J. et al. Design of Tough 3D Printable Elastomers with Human-in-the-Loop Reinforcement Learning. Preprint at https://doi.org/10.26434/chemrxiv-2025-w1563 (2025).
1.
Yang, Y., Taherian, H., Kalkhorani, V. A. & Wang, D. Elevating Robust Multi-Talker ASR by Decoupling Speaker Separation and Speech Recognition. Preprint at https://doi.org/10.48550/ARXIV.2503.17886 (2025).
1.
Yan, J. & Snir, M. Contemplating a Lightweight Communication Interface for Asynchronous Many-Task Systems. Preprint at https://doi.org/10.48550/ARXIV.2503.15400 (2025).
1.
Huang, H.-K., Park, S., Villa, U., Wang, L. V. & Anastasio, M. A. Gradient-free joint reconstruction of initial pressure distribution and wave speeds in transcranial photoacoustic computed tomography. in Photons Plus Ultrasound: Imaging and Sensing 2025 vol. 13319 97–103 (SPIE, 2025).
1.
Pant, S. et al. Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies. J. Phys. Chem. B acs.jpcb.5c00104 (2025) http://doi.org/10.1021/acs.jpcb.5c00104.
1.
Hu, Y. et al. Empirical Privacy Variance. Preprint at https://doi.org/10.48550/ARXIV.2503.12314 (2025).
1.
Chung, A. K.-W., Lam, K. K.-H. & Yunes, N. Quasinormal mode frequencies and gravitational perturbations of spinning black holes in modified gravity through METRICS: The dynamical Chern-Simons gravity case. Preprint at https://doi.org/10.48550/ARXIV.2503.11759 (2025).
1.
Yan, J., Kaiser, H. & Snir, M. Understanding the Communication Needs of Asynchronous Many-Task Systems -- A Case Study of HPX+LCI. Preprint at https://doi.org/10.48550/ARXIV.2503.12774 (2025).
1.
Merzky, A. et al. Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications. Preprint at https://doi.org/10.48550/ARXIV.2503.13343 (2025).
1.
Cui, S. et al. Characterizing GPU Resilience and Impact on AI/HPC Systems. Preprint at https://doi.org/10.48550/ARXIV.2503.11901 (2025).
1.
You, Z. & Guo, Y. PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation. Preprint at https://doi.org/10.48550/ARXIV.2503.08890 (2025).
1.
Balaji, P. et al. Quantum Circuits for SU(3) Lattice Gauge Theory. Preprint at https://doi.org/10.48550/ARXIV.2503.08866 (2025).
1.
Sasidharan, A., Xian-He, Lofstead, J. & Klasky, S. Performance Models for a Two-tiered Storage System. Preprint at https://doi.org/10.48550/ARXIV.2503.08966 (2025).
1.
Prather, B. S. KHARMA: Flexible, Portable Performance for GRMHD. Preprint at https://doi.org/10.48550/ARXIV.2408.01361 (2024).
1.
Arora, S. et al. ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems. Preprint at https://doi.org/10.48550/ARXIV.2503.08533 (2025).
1.
Wilfong, B. et al. MFC 5.0: An exascale many-physics flow solver. Preprint at https://doi.org/10.48550/ARXIV.2503.07953 (2025).
1.
Kearns, F. L. et al. D614G reshapes allosteric networks and opening mechanisms of SARS-CoV-2 spikes. Preprint at https://doi.org/10.1101/2025.03.07.642081 (2025).
1.
Yang, Y., Taherian, H., Kalkhoriani, V. A. & Wang, D. Elevating Robust ASR By Decoupling Multi-Channel Speaker Separation and Speech Recognition. in ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Hyderabad, India, 2025). doi:https://doi.org/10.1109/ICASSP49660.2025.10888074.
1.
Chung, J., Zhang, C. & Chen, T. Mobility Scooter Riding Behavior Stability Analysis Based on Multimodal Contrastive Learning. in 2024 IEEE International Conference on Big Data (BigData) 6439–6445 (IEEE, Washington, DC, USA, 2024). http://doi.org/10.1109/BigData62323.2024.10825478.
1.
Hossain, R. et al. Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators. npj Mater Degrad 9, 21 (2025).
1.
Pulavarthi, V., Nandal, D., Dan, S. & Pal, D. Are LLMs Ready for Practical Adoption for Assertion Generation? Preprint at https://doi.org/10.48550/ARXIV.2502.20633 (2025).
1.
Marques, J. M. C. et al. Map Space Belief Prediction for Manipulation-Enhanced Mapping. Preprint at https://doi.org/10.48550/ARXIV.2502.20606 (2025).
1.
Singer, L. P. et al. Optimal Follow-Up of Gravitational-Wave Events with the UltraViolet EXplorer (UVEX). Preprint at https://doi.org/10.48550/ARXIV.2502.17560 (2025).
1.
Tajwar, F. et al. Training a Generally Curious Agent. Preprint at https://doi.org/10.48550/ARXIV.2502.17543 (2025).